R/RcppExports.R

In BayesMRA: Bayesian Multi-Resolution Gaussian Process Approximations

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' A function for sampling from conditional multivariate normal distributions with mean A^{-1}b and covariance matrix A^{-1}.
#'
#' @param A \code{A} A \eqn{d \times d} \code{matrix} for the Gaussian full conditional distribution precision matrix.
#' @param b \code{b} A \eqn{d} \code{vector} for the Gaussian full conditional distribution mean.
#'
#' @examples
#' set.seed(111)
#' A <- diag(4)
#' b <- rnorm(4)
#' sample <- rmvn_arma(A, b)
#'
#' @export
rmvn_arma <- function(A, b) {
    .Call('_BayesMRA_rmvn_arma', PACKAGE = 'BayesMRA', A, b)
}

#' A function for sampling from conditional multivariate normal distributions with mean A^{-1}b and covariance matrix A^{-1}.
#'
#' @param A_chol \code{A} A \eqn{d \times d} \code{matrix} for the Gaussian full conditional distribution precision matrix Cholesky factor.
#' @param b \code{b} A \eqn{d} \code{vector} for the Gaussian full conditional distribution mean.
#'
#' @examples
#' set.seed(111)
#' A <- diag(4)
#' A_chol <- chol(A)
#' b <- rnorm(4)
#' sample <- rmvn_arma_chol(A_chol, b)
#'
#' @export
rmvn_arma_chol <- function(A_chol, b) {
    .Call('_BayesMRA_rmvn_arma_chol', PACKAGE = 'BayesMRA', A_chol, b)
}

#' A function for sampling from conditional multivariate normal distributions with mean A^{-1}b and covariance matrix A^{-1}.
#'
#' @param a \code{a} A scalar for the Gaussian full conditional distribution precision.
#' @param b \code{b} A \eqn{d} \code{vector} for the Gaussian full conditional distribution mean.
#'
#' @examples
#' set.seed(111)
#' a <- 4
#' b <- rnorm(1)
#' sample <- rmvn_arma_scalar(a, b)
#'
#' @export
rmvn_arma_scalar <- function(a, b) {
    .Call('_BayesMRA_rmvn_arma_scalar', PACKAGE = 'BayesMRA', a, b)
}

# Register entry points for exported C++ functions
methods::setLoadAction(function(ns) {
    .Call('_BayesMRA_RcppExport_registerCCallable', PACKAGE = 'BayesMRA')
})